Max-Stretch Reduction for Tree Spanners

A tree t-spanner T of a graph G is a spanning tree of G whose max-stretch is t, i.e., the distance between any two vertices in T is at most t times their distance in G. If G has a tree t-spanner but not a tree (t−1)-spanner, then G is said to have max-stretch of t. In this paper, we study the Max-Stretch Reduction Problem: for an unweighted graph G=(V,E), find a set of edges not in E originally whose insertion into G can decrease the max-stretch of G. Our results are as follows: (i) For a ring graph, we give a linear-time algorithm which inserts k edges improving the max-stretch optimally. (ii) For a grid graph, we give a nearly optimal max-stretch reduction algorithm which preserves the structure of the grid. (iii) In the general case, we show that it is -hard to decide, for a given graph G and its spanning tree of max-stretch t, whether or not one-edge insertion can decrease the max-stretch to t−1. (iv) Finally, we show that the max-stretch of an arbitrary graph on n vertices can be reduced to s′≥2 by inserting O(n/s′) edges, which can be determined in linear time, and observe that this number of edges is optimal up to a constant.     

Efficient construction of a bounded-degree spanner with low weight

LetS be a set ofn points in ℝ ^d and lett>1 be a real number. At-spanner forS is a graph having the points ofS as its vertices such that for any pairp, q of points there is a path between them of length at mostt times the Euclidean distance betweenp andq. An efficient implementation of a greedy algorithm is given that constructs at-spanner having bounded degree such that the total length of all its edges is bounded byO (logn) times the length of a minimum spanning tree forS. The algorithm has running timeO (n log ^d n). Applying recent results of Das, Narasimhan, and Salowe to thist-spanner gives anO(n log ^d n)-time algorithm for constructing at-spanner having bounded degree and whose total edge length is proportional to the length of a minimum spanning tree forS. Previously, noo(n ²)-time algorithms were known for constructing at-spanner of bounded degree. In the final part of the paper, an application to the problem of distance enumeration is given. This work was supported by the ESPRIT Basic Research Actions Program, under Contract No. 7141 (Project ALCOM II).     

Labeling Schemes for Dynamic Tree Networks

Distance labeling schemes are composed of a marker algorithm for labeling the vertices of a graph with short labels, coupled with a decoder algorithm allowing one to compute the distance between any two vertices directly from their labels (without using any additional information). As applications for distance labeling schemes concern mainly large and dynamically changing networks, it is of interest to study distributed dynamic labeling schemes. The current paper considers the problem on dynamic trees, and proposes efficient distributed schemes for it. The paper first presents a labeling scheme for distances in the dynamic tree model, with amortized message complexity O(log² n) per operation, where n is the size of the tree at the time the operation takes place. The protocol maintains O(log² n) bit labels. This label size is known to be optimal even in the static scenario. A more general labeling scheme is then introduced for the dynamic tree model, based on extending an existing static tree labeling scheme to the dynamic setting. The approach fits a number of natural tree functions, such as distance, separation level, and flow. The main resulting scheme incurs an overhead of an O(log n) multiplicative factor in both the label size and amortized message complexity in the case of dynamically growing trees (with no vertex deletions). If an upper bound on n is known in advance, this method yields a different tradeoff, with an O(log² n/log log n) multiplicative overhead on the label size but only an O(log n/log log n) overhead on the amortized message complexity. In the fully dynamic model the scheme also incurs an increased additive overhead in amortized communication, of O(log² n) messages per operation.     

Efficient Parallel Algorithms for Permutation Graphs

In this paper, we present optimal O(log n) time, O(n/log n) processor EREW PRAM parallel algorithms for finding the connected components, cut vertices, and bridges of a permutation graph. We also present an O(log n) time, O(n) processor, CREW PRAM model parallel algorithm for finding a Breadth First Search (BFS) spanning tree of a permutation graph rooted at vertex 1 and use the same to derive an efficient parallel algorithm for the All Pairs Shortest Path problem on permutation graphs.     

Additive Spanners and Distance and Routing Labeling Schemes for Hyperbolic Graphs

δ-Hyperbolic metric spaces have been defined by M. Gromov in 1987 via a simple 4-point condition: for any four points u,v,w,x, the two larger of the distance sums d(u,v)+d(w,x),d(u,w)+d(v,x),d(u,x)+d(v,w) differ by at most?2δ. They play an important role in geometric group theory, geometry of negatively curved spaces, and have recently become of interest in several domains of computer science, including algorithms and networking. In this paper, we study unweighted δ-hyperbolic graphs. Using the Layering Partition technique, we show that every n-vertex δ-hyperbolic graph with δ≥1/2 has an additive O(δlog?n)-spanner with at most O(δn) edges and provide a simpler, in our opinion, and faster construction of distance approximating trees of δ-hyperbolic graphs with an additive error O(δlog?n). The construction of our tree takes only linear time in the size of the input graph. As a consequence, we show that the family of n-vertex δ-hyperbolic graphs with δ≥1/2 admits a routing labeling scheme with O(δlog?² n) bit labels, O(δlog?n) additive stretch and O(log?₂(4δ)) time routing protocol, and a distance labeling scheme with O(log?² n) bit labels, O(δlog?n) additive error and constant time distance decoder.     

Isomorphic tree spanner problems

A treet-spanner, wheret is a positive integer, of a graph G is a spanning tree in which the distance between the two ends of every edge ofG is at mostt. This notion is motivated by applications in distributed systems and communication networks. This paper is concerned with the problem of determining whether a graph contains a tree t-spanner isomorphic to a given tree. It is shown that the problem fort=2 is solvable in O(n^3.5) time for 2-connected graphs, whereas the problem for any fixed integert 3 is NP-complete, even for 2-connected bipartite graphs.Financial support for this research was received from the Natural Sciences and Engineering Research Council of Canada.     

Finding a maximum matching in a permutation graph

We present anO(n log logn) time algorithm for finding a maximum matching in a permutation graph withn vertices, assuming that the input graph is represented by a permutation.     

Collective Tree Spanners in Graphs with Bounded Parameters

In this paper we study collective additive tree spanners for special families of graphs including planar graphs, graphs with bounded genus, graphs with bounded tree-width, graphs with bounded clique-width, and graphs with bounded chordality. We say that a graph G=(V,E) admits a system of μ collective additive tree r -spanners if there is a system $\mathcal{T}(G)In this paper we study collective additive tree spanners for special families of graphs including planar graphs, graphs with bounded genus, graphs with bounded tree-width, graphs with bounded clique-width, and graphs with bounded chordality. We say that a graph G=(V,E) admits a system of μ collective additive tree r -spanners if there is a system T(G)\mathcal{T}(G) of at most μ spanning trees of G such that for any two vertices x,y of G a spanning tree T ? T(G)T\in\mathcal{T}(G) exists such that d _T (x,y)≤d _G (x,y)+r. We describe a general method for constructing a “small” system of collective additive tree r-spanners with small values of r for “well” decomposable graphs, and as a byproduct show (among other results) that any weighted planar graph admits a system of O(?n)O(\sqrt{n}) collective additive tree 0-spanners, any weighted graph with tree-width at most k−1 admits a system of klog ₂ n collective additive tree 0-spanners, any weighted graph with clique-width at most k admits a system of klog _3/2 n collective additive tree (2w)(2\mathsf{w}) -spanners, and any weighted graph with size of largest induced cycle at most c admits a system of log ₂ n collective additive tree (2?c/2?w)(2\lfloor c/2\rfloor\mathsf{w}) -spanners and a system of 4log ₂ n collective additive tree (2(?c/3?+1)w)(2(\lfloor c/3\rfloor +1)\mathsf {w}) -spanners (here, w\mathsf{w} is the maximum edge weight in G). The latter result is refined for weighted weakly chordal graphs: any such graph admits a system of 4log ₂ n collective additive tree (2w)(2\mathsf{w}) -spanners. Furthermore, based on this collection of trees, we derive a compact and efficient routing scheme for those families of graphs.     

The Swap Edges of a Multiple-Sources Routing Tree

Let T be a spanning tree of a graph G and S⊂V(G) be a set of sources. The routing cost of T is the total distance from all sources to all vertices. For an edge e of T, the swap edge of e is the edge f minimizing the routing cost of the tree formed by replacing e with f. Given an undirected graph G and a spanning tree T of G, we investigate the problem of finding the swap edge for every tree edge. In this paper, we propose an O(mlog n+n ²)-time algorithm for the case of two sources and an O(mn)-time algorithm for the case of more than two sources, where m and n are the numbers of edges and vertices of G, respectively.     

Efficient algorithms for constructing (1+∊,β)-spanners in the distributed and streaming models

For an unweighted undirected graph G = (V,E), and a pair of positive integers α ≥ 1, β ≥ 0, a subgraph G′ = (V,H), H ⊂eqE, is called an (α,β)-spanner of G if for every pair of vertices u,v ∊ V, dist_G′(u,v) ≤ α ⋅ dist_G(u,v) + β. It was shown in [21] that for any ∊ > 0, κ = 1,2,…, there exists an integer β = β(∊,κ) such that for every n-vertex graph G there exists a (1+∊,β)-spanner G′ with O(n^1+1/κ) edges. An efficient distributed protocol for constructing (1+∊,β)-spanners was devised in [19]. The running time and the communication complexity of that protocol are O(n^1+ρ) and O(|E|n^ρ), respectively, where ρ is an additional control parameter of the protocol that affects only the additive term β. In this paper we devise a protocol with a drastically improved running time (O(n^ρ) as opposed to O(n^1+ρ)) for constructing (1+∊,β)-spanners. Our protocol has the same communication complexity as the protocol of [19], and it constructs spanners with essentially the same properties as the spanners that are constructed by the protocol of [19]. The protocol can be easily extended to a parallel implementation which runs in O(log n + (|E|⋅ n^ρlog n)/p) time using p processors in the EREW PRAM model. In particular, when the number of processors, p, is at least |E|⋅ n^ρ, the running time of the algorithm is O(log n). We also show that our protocol for constructing (1+∊,β)-spanners can be adapted to the streaming model, and devise a streaming algorithm that uses a constant number of passes and O(n^1+1/κ⋅ {log} n) bits of space for computing all-pairs-almost-shortest-paths of length at most by a multiplicative factor (1+∊) and an additive term of β greater than the shortest paths. Our algorithm processes each edge in time O(n^ρ), for an arbitrarily small ρ > 0. The only previously known algorithm for the problem [23] constructs paths of length κ times greater than the shortest paths, has the same space requirements as our algorithm, but requires O(n^1+1/κ) time for processing each edge of the input graph. However, the algorithm of [23] uses just one pass over the input, as opposed to the constant number of passes in our algorithm. We also show that any streaming algorithm for o(n)-approximate distance computation requires Ω(n) bits of space. This work was Supported by the DoD University Research Initiative (URI) administered by the Office of Naval Research under Grant N00014-01-1-0795. Michael Elkin was supported by ONR grant N00014-01-1-0795. Jian Zhang was supported by ONR grant N00014-01-1-0795 and NSF grants CCR-0105337 and ITR-0331548. Preliminary version of this paper was published in PODC’04, see [22]. After the preliminary version of our paper [22] appeared on PODC’04, Feigenbaum et al. [24] came up with a new streaming algorithm for the problem that is far more efficient than [23] in terms of time-per-edge processing. However, our algorithm is still the only existing streaming algorithm that provides an almost additive approximation of distances.     

Fully Dynamic Geometric Spanners

In this paper we present an algorithm for maintaining a spanner over a dynamic set of points in constant doubling dimension metric spaces. For a set S of points in ? ^d , a t-spanner is a sparse graph on the points of S such that there is a path in the spanner between any pair of points whose total length is at most t times the distance between the points. We present the first fully dynamic algorithm for maintaining a spanner whose update time depends solely on the number of points in S. In particular, we show how to maintain a (1+ε)-spanner with O(n/ε ^d ) edges, where points can be inserted to S in an amortized update time of O(log?n) and deleted from S in an amortized update time of $\tilde{O}(n^{1/3})$ . As a by-product of our techniques we obtain a simple incremental algorithm for constructing a (1+ε)-spanner with O(n/ε ^d ) edges in constant doubling dimension metric spaces whose running time is O(nlog?n).     

Distributed algorithms for ultrasparse spanners and linear size skeletons

We present efficient algorithms for computing very sparse low distortion spanners in distributed networks and prove some non-trivial lower bounds on the tradeoff between time, sparseness, and distortion. All of our algorithms assume a synchronized distributed network, where relatively short messages may be communicated in each time step. Our first result is a fast distributed algorithm for finding an ${O(2^{{\rm log}^{*} n} {\rm log} n)}We present efficient algorithms for computing very sparse low distortion spanners in distributed networks and prove some non-trivial lower bounds on the tradeoff between time, sparseness, and distortion. All of our algorithms assume a synchronized distributed network, where relatively short messages may be communicated in each time step. Our first result is a fast distributed algorithm for finding an O(2^{log* n} log n){O(2^{{\rm log}^{*} n} {\rm log} n)} -spanner with size O(n). Besides being nearly optimal in time and distortion, this algorithm appears to be the first that constructs an O(n)-size skeleton without requiring unbounded length messages or time proportional to the diameter of the network. Our second result is a new class of efficiently constructible (α, β)-spanners called Fibonacci spanners whose distortion improves with the distance being approximated. At their sparsest Fibonacci spanners can have nearly linear size, namely O(n(loglogn)^f){O(n(\log \log n)^{\phi})} , where f = (1 + ?5)/2{\phi = (1 + \sqrt{5})/2} is the golden ratio. As the distance increases the multiplicative distortion of a Fibonacci spanner passes through four discrete stages, moving from logarithmic to log-logarithmic, then into a period where it is constant, tending to 3, followed by another period tending to 1. On the lower bound side we prove that many recent sequential spanner constructions have no efficient counterparts in distributed networks, even if the desired distortion only needs to be achieved on the average or for a tiny fraction of the vertices. In particular, any distance preservers, purely additive spanners, or spanners with sublinear additive distortion must either be very dense, slow to construct, or have very weak guarantees on distortion.     

An Optimal Shortest Path Parallel Algorithm for Permutation Graphs

We present an optimal parallel algorithm for the single-source shortest path problem for permutation graphs. The algorithm runs in O(log n) time using O(n/log n) processors on an EREW PRAM. As an application, we show that a minimum connected dominating set in a permutation graph can be found in O(log n) time using O(n/log n) processors.     

A fast distributed approximation algorithm for minimum spanning trees

We present a distributed algorithm that constructs an O(log n)-approximate minimum spanning tree (MST) in any arbitrary network. This algorithm runs in time Õ(D(G) + L(G, w)) where L(G, w) is a parameter called the local shortest path diameter and D(G) is the (unweighted) diameter of the graph. Our algorithm is existentially optimal (up to polylogarithmic factors), i.e., there exist graphs which need Ω(D(G) + L(G, w)) time to compute an H-approximation to the MST for any $H\,\in\,[1, \Theta({\rm log} n)]We present a distributed algorithm that constructs an O(log n)-approximate minimum spanning tree (MST) in any arbitrary network. This algorithm runs in time ?(D(G) + L(G, w)) where L(G, w) is a parameter called the local shortest path diameter and D(G) is the (unweighted) diameter of the graph. Our algorithm is existentially optimal (up to polylogarithmic factors), i.e., there exist graphs which need Ω(D(G) + L(G, w)) time to compute an H-approximation to the MST for any . Our result also shows that there can be a significant time gap between exact and approximate MST computation: there exists graphs in which the running time of our approximation algorithm is exponentially faster than the time-optimal distributed algorithm that computes the MST. Finally, we show that our algorithm can be used to find an approximate MST in wireless networks and in random weighted networks in almost optimal ?(D(G)) time.     

Restrictions of Minimum Spanner Problems

At-spanner of a graphGis a spanning subgraphHsuch that the distance between any two vertices inHis at mostttimes their distance inG. Spanners arise in the context of approximating the original graph with a sparse subgraph (Peleg, D., and Schäffer, A. A. (1989),J. Graph. Theory13(1), 99–116). The MINIMUMt-SPANNER problem seeks to find at-spanner with the minimum number of edges for the given graph. In this paper, we completely settle the complexity status of this problem for various values oft, on chordal graphs, split graphs, bipartite graphs and convex bipartite graphs. Our results settle an open question raised by L. Cai (1994,Discrete Appl. Math.48, 187–194) and also greatly simplify some of the proofs presented by Cai and by L. Cai and M. Keil (1994,Networks24, 233–249). We also give a factor 2 approximation algorithm for the MINIMUM 2-SPANNER problem on interval graphs. Finally, we provide approximation algorithms for the bandwidth minimization problem on convex bipartite graphs and split graphs using the notion of tree spanners.     

An efficient distributed algorithm for finding all hinge vertices in networks

Let G=(V, E) be a graph with vertex set V of size n and edge set E of size m. A vertex v∈V is called a hinge vertex if there exist two vertices in V\{v} such that their distance becomes longer when v is removed. In this paper, we present a distributed algorithm that finds all hinge vertices on an arbitrary graph. The proposed algorithm works for named static asynchronous networks and achieves O(n ²) time complexity and O(m) message complexity. In particular, the total messages exchanged during the algorithm are at most 2m(log n+nlog n+1) bits.     

Finding Maximum Edge Bicliques in Convex Bipartite Graphs

A bipartite graph G=(A,B,E) is convex on B if there exists an ordering of the vertices of B such that for any vertex v??A, vertices adjacent to v are consecutive in?B. A complete bipartite subgraph of a graph G is called a biclique of G. Motivated by an application to analyzing DNA microarray data, we study the problem of finding maximum edge bicliques in convex bipartite graphs. Given a bipartite graph G=(A,B,E) which is convex on B, we present a new algorithm that computes a maximum edge biclique of G in O(nlog?³ nlog?log?n) time and O(n) space, where n=|A|. This improves the current O(n ²) time bound available for the problem. We also show that for two special subclasses of convex bipartite graphs, namely for biconvex graphs and bipartite permutation graphs, a maximum edge biclique can be computed in O(n??(n)) and O(n) time, respectively, where n=min?(|A|,|B|) and ??(n) is the slowly growing inverse of the Ackermann function.     

A Random Base Change Algorithm for Permutation Groups

A new random base change algorithm is presented for a permutation group G acting on n points whose worst case asymptotic running time is better for groups with a small to moderate size base than any known deterministic algorithm. To achieve this time bound, the algorithm requires a random generator Rand(G) producing a random element of G with the uniform distribution and so that the time for each call to Rand (G) is bounded by some function f(n, G). The random base change algorithm has probability 1 - 1/|G| of completing in time O(f(n, G) log |G|) and outputting a data structure for representing the point stabilizer sequence relative to the new ordering. The algorithm requires O(n log |G|) space and the data structure produced can be used to test group membership in time O(n log |G|). Since the output of this algorithm is a data structure allowing generation of random group elements in time O(n log |G|), repeated application of the random base change algorithms for different orderings of the permutation domain of G will always run in time O (n log² |G|). An earlier version of this work appeared in Cooperman and Finkelstein (1992b).     

A Simple Optimal Parallel Algorithm for a Core of a Tree

A core of a tree T = (V, E) is a path in T which minimizes ∑_vVd(v, P), where d(v, P), the distance from a vertex v to path P, is defined as min_uPd(v, u). We present an optimal parallel algorithm to find a core of T in O(log n) time using O(n/log n) processors on an EREW PRAM machine, where n is the number of vertices of tree T.     

Spanners in sparse graphs

A t-spanner of a graph G is a spanning subgraph S in which the distance between every pair of vertices is at most t times their distance in G. If S is required to be a tree then S is called a tree t-spanner of G. In 1998, Fekete and Kremer showed that on unweighted planar graphs deciding whether G admits a tree t-spanner is polynomial time solvable for t?3 and is NP-complete when t is part of the input. They also left as an open problem if the problem is polynomial time solvable for every fixed t?4. In this work we resolve the open question of Fekete and Kremer by proving much more general results:

• • 

  The problem of finding a t-spanner of treewidth at most k in a given planar graph G is fixed parameter tractable parameterized by k and t. Moreover, for every fixed t and k, the running time of our algorithm is linear.

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  Our technique allows to extend the result from planar graphs to much more general classes of graphs. An apex graph is a graph that can be made planar by the removal of a single vertex. We prove that the problem of finding a t-spanner of treewidth k is fixed parameter tractable on graphs that do not contain some fixed apex graph as a minor, i.e. on apex-minor-free graphs. The class of apex-minor-free graphs contains planar graphs and graphs of bounded genus.

• • 

  Finally, we show that the tractability border of the t-spanner problem cannot be extended beyond the class of apex-minor-free graphs and in this sense our results are tight. In particular, for every t?4, the problem of finding a tree t-spanner is NP-complete on K6-minor-free graphs.